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Evidence-Based Management of the Anzali Wetland System (Northern Iran) Based on Innovative Monitoring and Modeling Methods

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  • Roghayeh Sadeghi Pasvisheh

    (Department of Animal Sciences and Aquatic Ecology, Ghent University, 9000 Ghent, Belgium)

  • Marie Anne Eurie Forio

    (Department of Animal Sciences and Aquatic Ecology, Ghent University, 9000 Ghent, Belgium)

  • Long Tuan Ho

    (Department of Animal Sciences and Aquatic Ecology, Ghent University, 9000 Ghent, Belgium)

  • Peter L. M. Goethals

    (Department of Animal Sciences and Aquatic Ecology, Ghent University, 9000 Ghent, Belgium)

Abstract

As an “international aquatic ecosystem” in Northern Iran, the Anzali wetland is a nursery for fish and a breeding and wintering area for a wide variety of waterfowl. The wetland is threatened by human activities (deforestation, hunting, tourism, and urbanization), leading to habitat destruction, eutrophication, and sediment accumulation. To stop the degradation and to set up effective protection and restoration in line with the Sustainable Development Goals, scientific insights must be integrated into a practical framework for evidence-based support for policymakers and managers of the Anzali wetland. In this study, the Drivers–Pressure–State–Impact–Response (DPSIR) framework is used as a suitable tool to link human pressures and state changes to derive an overview of the potential impacts. Population growth, intensive agriculture, increased urbanization, and industrialization are the major driving forces that have led to a complex cascade of state changes. For instance, during recent years, water quality deterioration, habitat degradation, and the overgrowth of invasive species in the Anzali wetland watershed have caused negative socio-economic and human health impacts. Integrated and innovative monitoring programs combined with socio-environmental modeling techniques are needed for a more evidence-based management approach as part of a multiresponse strategy for the sustainable development of the wetland system. In this respect, there is a critical gap in useful information concerning biological composition and innovative monitoring methods. Moreover, the relation of biota with human activity and environmental conditions needs to be better quantified. Therefore, ecological modeling techniques based on machine learning and statistics were reviewed for their advantages and disadvantages. The overview of approaches presented here can serve as the basis for scientists, practitioners, and decision-makers to develop and implement evidence-based management programs for the Anzali wetland.

Suggested Citation

  • Roghayeh Sadeghi Pasvisheh & Marie Anne Eurie Forio & Long Tuan Ho & Peter L. M. Goethals, 2021. "Evidence-Based Management of the Anzali Wetland System (Northern Iran) Based on Innovative Monitoring and Modeling Methods," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5503-:d:554724
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    References listed on IDEAS

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    1. Shewit Gebremedhin & Abebe Getahun & Wassie Anteneh & Stijn Bruneel & Peter Goethals, 2018. "A Drivers-Pressure-State-Impact-Responses Framework to Support the Sustainability of Fish and Fisheries in Lake Tana, Ethiopia," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    2. Forio, Marie Anne Eurie & Mouton, Ans & Lock, Koen & Boets, Pieter & Nguyen, Thi Hanh Tien & Damanik Ambarita, Minar Naomi & Musonge, Peace Liz Sasha & Dominguez-Granda, Luis & Goethals, Peter L.M., 2017. "Fuzzy modelling to identify key drivers of ecological water quality to support decision and policy making," Environmental Science & Policy, Elsevier, vol. 68(C), pages 58-68.
    3. Sadeghi, Roghayeh & Zarkami, Rahmat & Sabetraftar, Karim & Van Damme, Patrick, 2012. "Application of classification trees to model the distribution pattern of a new exotic species Azolla filiculoides (Lam.) at Selkeh Wildlife Refuge, Anzali wetland, Iran," Ecological Modelling, Elsevier, vol. 243(C), pages 8-17.
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    5. Sadeghi, Roghayeh & Zarkami, Rahmat & Van Damme, Patrick, 2014. "Modelling habitat preference of an alien aquatic fern, Azolla filiculoides (Lam.), in Anzali wetland (Iran) using data-driven methods," Ecological Modelling, Elsevier, vol. 284(C), pages 1-9.
    6. Meisam Jafari & Hamid Majedi & Seyed Masoud Monavari & Ali Asghar Alesheikh & Mirmasoud Kheirkhah Zarkesh, 2016. "Dynamic Simulation of Urban Expansion Based on Cellular Automata and Logistic Regression Model: Case Study of the Hyrcanian Region of Iran," Sustainability, MDPI, vol. 8(8), pages 1-18, August.
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    Cited by:

    1. Xinsheng Zhu & Yongfeng Yang & Jun Yuan & Ziru Niu, 2023. "Evaluation of the Ecological Status of Wetlands of International Importance in China," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    2. Zhaobin Li & Lei Ma & Dongmei Gou & Qianqian Hong & Leungkam Fai & Bo Xiong, 2022. "The Impact of Urban Development on Wetland Conservation," Sustainability, MDPI, vol. 14(21), pages 1-18, October.

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